mlsanityData to 7 Oct 2026

MultiSessionCollab: Learning User Preferences with Memory to Improve Long-Term Collaboration

arXiv 2601.02702 ↗First posted 6 Jan 2026cs.AIcited by 7

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Abstract (arXiv)

As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that is specifically designed to learn user preferences across sessions and improve interactions. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with our memory improves collaboration over time, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 35 of its 86 references that are in the map. First posted on arXiv 6 Jan 2026.

Builds on

35 of 86 references.

References · 35

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23 more
  1. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
  2. 2025LLMs Get Lost In Multi-Turn Conversationcited by 400
  3. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  4. 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 372
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  17. 2023Eliciting Human Preferences with Language Modelscited by 90
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  19. 2022Keep Me Updated! Memory Management in Long-term Conversationscited by 62
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026LLMs Corrupt Your Documents When You Delegatecited by 9
  2. 2026Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviorscited by 4
  3. 2026MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systemscited by 2
  4. 2026Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agentscited by 2
  5. 2026Agents in the Large: Perception-Centered Architecture for Persistent Agentscited by 1
  6. 2026Hypotheses-Guided Self Distillation for Continual Personalizationcited by 1
  7. 2026Re-Centering Humans in LLM Personalizationcited by 0